OCT-OCTA segmentation: combining structural and blood flow information to segment Bruch's membrane

ENLARGEMENT OF CHOROIDAL NEOVASCULARIZATION BEFORE RECURRENCE AFTER PHOTODYNAMIC THERAPY FOR PACHYCHOROID NEOVASCULOPATHY

PURPOSE

To investigate predictors of recurrent exudation in choroidal neovascularization (CNV) of pachychoroid neovasculopathy after photodynamic therapy (PDT).

METHODS

Consecutive, treatment-naïve, symptomatic patients with pachychoroid neovasculopathy with subfoveal retinal fluid treated with PDT and followed for 18 months were studied retrospectively. Choroidal neovascularization areas were calculated from optical coherence tomography angiography images obtained at various time points after the initial PDT.

RESULTS

In 52 eyes, the subfoveal retinal fluid resolved completely three months after PDT; in 23 (44%) eyes, exudation recurred during the 18-month follow-up period. In 29 eyes with no recurrence, the mean baseline square root of the CNV area of 1.91 mm (95% CI, 0.27) decreased significantly ( P = 0.006) to 1.47 mm (95% CI, 0.16) at three months after PDT and decreased further until 12 months after PDT (mean, 1.26 mm; 95% CI, P < 0.001) and was maintained thereafter. In 23 eyes with a recurrence, the square root of the CNV area enlarged significantly ( P = 0.028) from 1.43 mm (95% CI, 0.21) at examination three months before the recurrence to 1.73 mm (95% CI, 0.18) at recurrence.

CONCLUSION

Choroidal neovascularization enlargement during the follow-up period after PDT for pachychoroid neovasculopathy may predict recurrence.

Spectral-Domain and Swept-Source OCT Angiographic Scans Yield Similar Drusen Measurements When Processed with the Same Algorithm

Purpose

An algorithm developed to obtain drusen area and volume measurements using swept-source OCT angiography (SS-OCTA) scans was tested on spectral-domain OCT angiography (SD-OCTA) scans.

Design

Retrospective study.

Participants

Forty pairs of scans from 27 eyes with intermediate age-related macular degeneration and drusen.

Methods

Patients underwent both SD-OCTA and SS-OCTA imaging at the same visit using the 6 mm × 6 mm OCTA scan patterns. Using the same algorithm, we obtained drusen area and volume measurements within both 3 mm and 5 mm fovea-centered circles. Paired 2-sample t-tests were performed along with Pearson's correlation tests.

Main Outcome Measures

Mean square root (sqrt) drusen area and cube root (cbrt) drusen volume within the 3 mm and 5 mm fovea-centered circles.

Results

Mean sqrt drusen area values from SD-OCTA and SS-OCTA scans were 1.57 (standard deviation [SD] 0.57) mm and 1.49 (SD 0.58) mm in the 3 mm circle and 1.88 (SD 0.59) mm and 1.76 (SD 0.58) mm in the 5 mm circle, respectively. Mean cbrt drusen volume measurements were 0.54 (SD 0.19) mm and 0.51 (SD 0.20) mm in the 3 mm circle, and 0.60 (SD 0.17) mm and 0.57 (SD 0.17) mm in the 5 mm circle. Small differences in area and volume measurements were found (all P < 0.001); however, the correlations between the instruments were strong (all coefficients > 0.97; all P < 0.001).

Conclusions

An algorithm originally developed for SS-OCTA scans performs well when used to obtain drusen volume and area measurements from SD-OCTA scans; thus, a separate SD-OCT structural scan is unnecessary to obtain measurements of drusen.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

ARTIFICIAL INTELLIGENCE FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY-BASED DISEASE ACTIVITY PREDICTION IN AGE-RELATED MACULAR DEGENERATION

PURPOSE

The authors hypothesize that optical coherence tomography angiography (OCTA)-visualized vascular morphology may be a predictor of choroidal neovascularization status in age-related macular degeneration (AMD). The authors thus evaluated the use of artificial intelligence (AI) to predict different stages of AMD disease based on OCTA en face 2D projections scans.

METHODS

Retrospective cross-sectional study based on collected 2D OCTA data from 310 high-resolution scans. Based on OCT B-scan fluid and clinical status, OCTA was classified as normal, dry AMD, wet AMD active, and wet AMD in remission with no signs of activity. Two human experts graded the same test set, and a consensus grading between two experts was used for the prediction of four categories.

RESULTS

The AI can achieve 80.36% accuracy on a four-category grading task with 2D OCTA projections. The sensitivity of prediction by AI was 0.7857 (active), 0.7142 (remission), 0.9286 (dry AMD), and 0.9286 (normal) and the specificity was 0.9524, 0.9524, 0.9286, and 0.9524, respectively. The sensitivity of prediction by human experts was 0.4286 active choroidal neovascularization, 0.2143 remission, 0.8571 dry AMD, and 0.8571 normal with specificity of 0.7619, 0.9286, 0.7857, and 0.9762, respectively. The overall AI classification prediction was significantly better than the human (odds ratio = 1.95, P = 0.0021).

CONCLUSION

These data show that choroidal neovascularization morphology can be used to predict disease activity by AI; longitudinal studies are needed to better understand the evolution of choroidal neovascularization and features that predict reactivation. Future studies will be able to evaluate the additional predicative value of OCTA on top of other imaging characteristics (i.e., fluid location on OCT B scans) to help predict response to treatment.

Deep-learning-based automated measurement of outer retinal layer thickness for use in the assessment of age-related macular degeneration, applicable to both swept-source and spectral-domain OCT imaging

Effective biomarkers are required for assessing the progression of age-related macular degeneration (AMD), a prevalent and progressive eye disease. This paper presents a deep learning-based automated algorithm, applicable to both swept-source OCT (SS-OCT) and spectral-domain OCT (SD-OCT) scans, for measuring outer retinal layer (ORL) thickness as a surrogate biomarker for outer retinal degeneration, e.g., photoreceptor disruption, to assess AMD progression. The algorithm was developed based on a modified TransUNet model with clinically annotated retinal features manifested in the progression of AMD. The algorithm demonstrates a high accuracy with an intersection of union (IoU) of 0.9698 in the testing dataset for segmenting ORL using both SS-OCT and SD-OCT datasets. The robustness and applicability of the algorithm are indicated by strong correlation (r = 0.9551, P < 0.0001 in the central-fovea 3 mm-circle, and r = 0.9442, P < 0.0001 in the 5 mm-circle) and agreement (the mean bias = 0.5440 um in the 3-mm circle, and 1.392 um in the 5-mm circle) of the ORL thickness measurements between SS-OCT and SD-OCT scans. Comparative analysis reveals significant differences (P < 0.0001) in ORL thickness among 80 normal eyes, 30 intermediate AMD eyes with reticular pseudodrusen, 49 intermediate AMD eyes with drusen, and 40 late AMD eyes with geographic atrophy, highlighting its potential as an independent biomarker for predicting AMD progression. The findings provide valuable insights into the ORL alterations associated with different stages of AMD and emphasize the potential of ORL thickness as a sensitive indicator of AMD severity and progression.

Robust AMD Stage Grading with Exclusively OCTA Modality Leveraging 3D Volume

Age-related Macular Degeneration (AMD) is a degenerative eye disease that causes central vision loss. Optical Coherence Tomography Angiography (OCTA) is an emerging imaging modality that aids in the diagnosis of AMD by displaying the pathogenic vessels in the subretinal space. In this paper, we investigate the effectiveness of OCTA from the view of deep classifiers. To the best of our knowledge, this is the first study that solely uses OCTA for AMD stage grading. By developing a 2D classifier based on OCTA projections, we identify that segmentation errors in retinal layers significantly affect the accuracy of classification. To address this issue, we propose analyzing 3D OCTA volumes directly using a 2D convolutional neural network trained with additional projection supervision. Our experimental results show that we achieve over 80% accuracy on a four-stage grading task on both error-free and error-prone test sets, which is significantly higher than 60%, the accuracy of human experts. This demonstrates that OCTA provides sufficient information for AMD stage grading and the proposed 3D volume analyzer is more robust when dealing with OCTA data with segmentation errors.

OCT angiography and its retinal biomarkers [Invited]

Optical coherence tomography angiography (OCTA) is a high-resolution, depth-resolved imaging modality with important applications in ophthalmic practice. An extension of structural OCT, OCTA enables non-invasive, high-contrast imaging of retinal and choroidal vasculature that are amenable to quantification. As such, OCTA offers the capability to identify and characterize biomarkers important for clinical practice and therapeutic research. Here, we review new methods for analyzing biomarkers and discuss new insights provided by OCTA.

Increased peripapillary capillaries in patients with acute leukemia by using optical coherence tomography angiography

PURPOSE

To evaluate the radial peripapillary capillary vessel density (RPC-VD) and thickness of the retinal nerve fiber layer (RNFL) in acute leukemia (AL) and the associations of these characteristics with blood laboratory parameters.

METHODS

A cross-sectional study was performed at the Ophthalmology Department of the Sun Yat-sen Memorial Hospital from February 2019 to April 2022. Sixty eyes of 30 patients diagnosed with AL and sixty eyes of 30 matched healthy controls were included. Optical coherence tomography angiography (OCTA) in the 4.5-mm Angio Disc scan mode and the Ganglion cell complex scan mode were performed for all participants. Correlation analyses were used to examine the associations of RPC-VD and RNFL with blood laboratory parameters.

RESULTS

Patients in the AL group had significantly increased RPC-VD in the whole-image (51.42±0.35 vs. 49.52±0.36) and peripapillary fields (53.90±0.43 vs. 51.17±0.50) compared with people in the control group (all P<0.001), while no difference was found for RPC-VD in the inside optic disc fields in the two groups. The RNFL in the AL group was significantly thicker than that in the control group (131.10±3.89 μm vs. 115.03±2.22 μm, P<0.05). Complete blood count (CBC) parameters, including red blood cells, hemoglobin and hematocrit, had a significant negative correlation with RPC-VD and RNFL (all P <0.05).

CONCLUSION

An increased RPC-VD and a thicker RNFL are evidence of fundus changes in patients with early-stage AL, and these metrics may be related to decreases in red blood cells, hemoglobin and hematocrit.

Bruch's Membrane: A Key Consideration with Complement-Based Therapies for Age-Related Macular Degeneration

The complement system is crucial for immune surveillance, providing the body's first line of defence against pathogens. However, an imbalance in its regulators can lead to inappropriate overactivation, resulting in diseases such as age-related macular degeneration (AMD), a leading cause of irreversible blindness globally affecting around 200 million people. Complement activation in AMD is believed to begin in the choriocapillaris, but it also plays a critical role in the subretinal and retinal pigment epithelium (RPE) spaces. Bruch's membrane (BrM) acts as a barrier between the retina/RPE and choroid, hindering complement protein diffusion. This impediment increases with age and AMD, leading to compartmentalisation of complement activation. In this review, we comprehensively examine the structure and function of BrM, including its age-related changes visible through in vivo imaging, and the consequences of complement dysfunction on AMD pathogenesis. We also explore the potential and limitations of various delivery routes (systemic, intravitreal, subretinal, and suprachoroidal) for safe and effective delivery of conventional and gene therapy-based complement inhibitors to treat AMD. Further research is needed to understand the diffusion of complement proteins across BrM and optimise therapeutic delivery to the retina.

Applications of Artificial Intelligence in Optical Coherence Tomography Angiography Imaging

Optical coherence tomography angiography (OCTA) and artificial intelligence (AI) are two emerging fields that complement each other. OCTA enables the noninvasive, in vivo, 3D visualization of retinal blood flow with a micrometer resolution, which has been impossible with other imaging modalities. As it does not need dye-based injections, it is also a safer procedure for patients. AI has excited great interest in many fields of daily life, by enabling automatic processing of huge amounts of data with a performance that greatly surpasses previous algorithms. It has been used in many breakthrough studies in recent years, such as the finding that AlphaGo can beat humans in the strategic board game of Go. This paper will give a short introduction into both fields and will then explore the manifold applications of AI in OCTA imaging that have been presented in the recent years. These range from signal generation over signal enhancement to interpretation tasks like segmentation and classification. In all these areas, AI-based algorithms have achieved state-of-the-art performance that has the potential to improve standard care in ophthalmology when integrated into the daily clinical routine.

Depth-resolved visualization and automated quantification of hyperreflective foci on OCT scans using optical attenuation coefficients

An automated depth-resolved algorithm using optical attenuation coefficients (OACs) was developed to visualize, localize, and quantify hyperreflective foci (HRF) seen on OCT imaging that are associated with macular hyperpigmentation and represent an increased risk of disease progression in age related macular degeneration. To achieve this, we first transformed the OCT scans to linear representation, which were then contrasted by OACs. HRF were visualized and localized within the entire scan by differentiating HRF within the retina from HRF along the retinal pigment epithelium (RPE). The total pigment burden was quantified using the en face sum projection of an OAC slab between the inner limiting membrane (ILM) to Bruch's membrane (BM). The manual total pigment burden measurements were also obtained by combining manual outlines of HRF in the B-scans with the total area of hypotransmission defects outlined on sub-RPE slabs, which was used as the reference to compare with those obtained from the automated algorithm. 6×6 mm swept-source OCT scans were collected from a total of 49 eyes from 42 patients with macular HRF. We demonstrate that the algorithm was able to automatically distinguish between HRF within the retina and HRF along the RPE. In 24 test eyes, the total pigment burden measurements by the automated algorithm were compared with measurements obtained from manual segmentations. A significant correlation was found between the total pigment area measurements from the automated and manual segmentations (P < 0.001). The proposed automated algorithm based on OACs should be useful in studying eye diseases involving HRF.

RPS-Net: An effective retinal image projection segmentation network for retinal vessels and foveal avascular zone based on OCTA data

BACKGROUND

Optical coherence tomography angiography (OCTA) is an advanced imaging technology that can present the three-dimensional (3D) structure of retinal vessels (RVs). Quantitative analysis of retinal vessel density and foveal avascular zone (FAZ) area is of great significance in clinical diagnosis and the automatic semantic segmentation at the pixel level helps quantitative analysis. The existing segmentation methods cannot effectively use the volume data and projection map data of the OCTA image at the same time and lack the trade-off between global perception and local details, which lead to problems such as discontinuity of segmentation results and deviation of morphological estimation.

PURPOSE

In order to better assist physicians in clinical diagnosis and treatment, the segmentation accuracy of RVs and FAZ needs to be further improved. In this work, we propose an effective retinal image projection segmentation network (RPS-Net) to achieve accurate RVs and FAZ segmentation. Experiments show that this network exhibits good performance and outperforms other existing methods.

METHODS

Our method considers three aspects. First, we use two parallel projection paths to learn global perceptual features and local supplementary details. Secondly, we use the dual-way projection learning module (DPLM) to reduce the depth of the 3D data and learn image spatial features. Finally, we merged the two-dimensional features learned from the volume data with the two-dimensional projection data, and used a U-shaped network to further learn and generate the final result.

RESULTS

We validated our model on the OCTA-500, which is a large multi-modal, multi-task retinal dataset. The experimental results showed that our method achieved state-of-the-art performance, the mean Dice coefficients for RVs are 89.89 ± 2.60 (%) and 91.40 ± 9.18 (%) on the two subsets, while the Dice coefficients for FAZ are 91.55 ± 2.05 (%) and 97.80 ± 2.75 (%), respectively.

CONCLUSIONS

Our method can make full use of the information of 3D data and 2D data to generate segmented images with higher continuity and accuracy. Code is available at https://github.com/hchuanZ/MFFN/tree/master. This article is protected by copyright. All rights reserved.

Validation of a Novel Automated Algorithm to Measure Drusen Volume and Area Using Swept Source Optical Coherence Tomography Angiography

Purpose

The purpose of this study was to validate a novel automated swept source optical coherence tomography angiography (SS-OCTA) algorithm to measure elevations of the retinal pigment epithelium (RPE) in eyes with nonexudative age-related macular degeneration (neAMD).

Methods

Patients with drusen were enrolled in a prospective optical coherence tomography (OCT) study and underwent both spectral domain OCT (SD-OCT) and SS-OCTA imaging at the same visit using the 6 × 6 mm scan patterns. The RPE elevation measurements (square root area and cube root volume) from the SS-OCTA algorithm were compared with the automated validated SD-OCT algorithm on the instrument. Standard deviations of drusen measurements from four repeated scans of another separate set were also calculated to evaluate the reproducibility of the SS-OCTA algorithm.

Results

A total of 53 eyes from 28 patients were scanned on both instruments. A very strong correlation was found between the measurements from the two algorithms (all r > 0.95), although the measurements of the drusen area and volume were all larger from the SS-OCTA instrument. The reproducibility of the new SS-OCTA algorithm was analyzed using a sample of 66 eyes from 43 patients. The intraclass correlation coefficient (ICC) was greater than 99% from different macular regions for both the square root area and cube root volume measurements.

Conclusions

A novel automated SS-OCTA algorithm for the quantitative assessment of drusen was validated against the SD-OCT algorithm and was shown to be highly reproducible.

Translational Relevance

This novel SS-OCTA algorithm provides a strategy to measure the area and volume of drusen to assess disease progression in neAMD.